ReinforcementLearningEnvironments.jl
Built-in Environments
Traits | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
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ActionStyle | MinimalActionSet | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||
FullActionSet | ✔ | ✔ | ||||||||||||
ChanceStyle | Stochastic | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||
Deterministic | ✔ | ✔ | ✔ | |||||||||||
ExplicitStochastic | ✔ | ✔ | ✔ | |||||||||||
DefaultStateStyle | Observation | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||
InformationSet | ✔ | ✔ | ||||||||||||
DynamicStyle | Simultaneous | ✔ | ||||||||||||
Sequential | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||
InformationStyle | PerfectInformation | ✔ | ✔ | ✔ | ||||||||||
ImperfectInformation | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||
NumAgentStyle | MultiAgent | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||||
SingleAgent | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||||||
RewardStyle | TerminalReward | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||
StepReward | ✔ | ✔ | ✔ | ✔ | ✔ | |||||||||
StateStyle | Observation | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ||
InformationSet | ✔ | ✔ | ||||||||||||
InternalState | ✔ | |||||||||||||
UtilityStyle | GeneralSum | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||||
ZeroSum | ✔ | ✔ | ✔ | |||||||||||
ConstantSum | ✔ | |||||||||||||
IdenticalUtility | ✔ |
Many traits are borrowed from OpenSpiel. |
3-rd Party Environments
Environment Name | Dependent Package Name | Description |
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Environments in this package support the interfaces defined in |
#
ReinforcementLearningEnvironments.KUHN_POKER_REWARD_TABLE
— Constant
#
ReinforcementLearningEnvironments.ActionTransformedEnv
— Method
ActionTransformedEnv(env;action_space_mapping=identity, action_mapping=identity)
action_space_mapping
will be applied to action_space(env)
and legal_action_space(env)
. action_mapping
will be applied to action
before feeding it into env
.
#
ReinforcementLearningEnvironments.BitFlippingEnv
— Type
In Bit Flipping Environment we have n bits. The actions are 1 to n where executing i-th action flips the i-th bit of the state. For every episode we sample uniformly and initial state as well as the target state.
Refer Hindsight Experience Replay paper for the motivation behind the environment.
#
ReinforcementLearningEnvironments.CartPoleEnv
— Method
CartPoleEnv(;kwargs...)
Keyword arguments
-
T = Float64
-
continuous = false
-
rng = Random.default_rng()
-
gravity = T(9.8)
-
masscart = T(1.0)
-
masspole = T(0.1)
-
halflength = T(0.5)
-
forcemag = T(10.0)
-
max_steps = 200
-
dt = 0.02
-
thetathreshold = 12.0 # degrees
-
xthreshold
= 2.4`
#
ReinforcementLearningEnvironments.DefaultStateStyleEnv
— Method
DefaultStateStyleEnv{S}(env::E)
Reset the result of DefaultStateStyle
without changing the original behavior.
#
ReinforcementLearningEnvironments.GraphShortestPathEnv
— Type
GraphShortestPathEnv([rng]; n=10, sparsity=0.1, max_steps=10)
Quoted A.3 in the the paper Decision Transformer: Reinforcement Learning via Sequence Modeling.
We give details of the illustrative example discussed in the introduction. The task is to find theshortest path on a fixed directed graph, which can be formulated as an MDP where reward is0whenthe agent is at the goal node and−1otherwise. The observation is the integer index of the graphnode the agent is in. The action is the integer index of the graph node to move to next. The transitiondynamics transport the agent to the action’s node index if there is an edge in the graph, while theagent remains at the past node otherwise. The returns-to-go in this problem correspond to negativepath lengths and maximizing them corresponds to generating shortest paths.
#
ReinforcementLearningEnvironments.KuhnPokerEnv
— Method
KuhnPokerEnv()
See more detailed description here.
Here we demonstrate how to write a typical ZERO_SUM
, IMPERFECT_INFORMATION
game. The implementation here has a explicit CHANCE_PLAYER
.
TODO: add public state for SPECTATOR
. Ref: https://arxiv.org/abs/1906.11110
#
ReinforcementLearningEnvironments.MaxTimeoutEnv
— Method
MaxTimeoutEnv(env::E, max_t::Int; current_t::Int = 1)
Force is_terminated(env)
return true
after max_t
interactions.
#
ReinforcementLearningEnvironments.MontyHallEnv
— Method
MontyHallEnv(;rng=Random.default_rng())
Quoted from wiki:
Suppose you’re on a game show, and you’re given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what’s behind the doors, opens another door, say No. 3, which has a goat. He then says to you, "Do you want to pick door No. 2?" Is it to your advantage to switch your choice?
Here we’ll introduce the first environment which is of FULL_ACTION_SET
.
#
ReinforcementLearningEnvironments.MountainCarEnv
— Method
MountainCarEnv(;kwargs...)
Keyword arguments
-
T = Float64
-
continuous = false
-
rng = Random.default_rng()
-
min_pos = -1.2
-
max_pos = 0.6
-
max_speed = 0.07
-
goal_pos = 0.5
-
max_steps = 200
-
goal_velocity = 0.0
-
power = 0.001
-
gravity = 0.0025
#
ReinforcementLearningEnvironments.MultiArmBanditsEnv
— Method
MultiArmBanditsEnv(;true_reward=0., k = 10,rng=Random.default_rng())
true_reward
is the expected reward. k
is the number of arms. See multi-armed bandit for more detailed explanation.
This is a one-shot game. The environment terminates immediately after taking in an action. Here we use it to demonstrate how to write a customized environment with only minimal interfaces defined.
#
ReinforcementLearningEnvironments.PendulumEnv
— Method
PendulumEnv(;kwargs...)
Keyword arguments
-
T = Float64
-
max_speed = T(8)
-
max_torque = T(2)
-
g = T(10)
-
m = T(1)
-
l = T(1)
-
dt = T(0.05)
-
max_steps = 200
-
continuous::Bool = true
-
n_actions::Int = 3
-
rng = Random.default_rng()
#
ReinforcementLearningEnvironments.PendulumNonInteractiveEnv
— Type
A non-interactive pendulum environment.
Accepts only nothing
actions, which result in the system being simulated for one time step. Sets env.done
to true
once maximum_time
is reached. Resets to a random position and momentum. Always returns zero rewards.
Useful for debugging and development purposes, particularly in model-based reinforcement learning.
#
ReinforcementLearningEnvironments.PendulumNonInteractiveEnv
— Method
PendulumNonInteractiveEnv(;kwargs...)
Keyword arguments
-
float_type = Float64
-
gravity = 9.8
-
length = 2.0
-
mass = 1.0
-
step_size = 0.01
-
maximum_time = 10.0
-
rng = Random.default_rng()
#
ReinforcementLearningEnvironments.RandomWalk1D
— Type
RandomWalk1D(;rewards=-1. => 1.0, N=7, start_pos=(N+1) ÷ 2, actions=[-1,1])
An agent is placed at the start_pos
and can move left or right (stride is defined in actions). The game terminates when the agent reaches either end and receives a reward correspondingly.
Compared to the MultiArmBanditsEnv
:
-
The state space is more complicated (well, not that complicated though).
-
It’s a sequential game of multiple action steps.
-
It’s a deterministic game instead of stochastic game.
#
ReinforcementLearningEnvironments.RewardOverriddenEnv
— Type
RewardOverriddenEnv(env, f)
Apply f
on env
to generate a custom reward.
#
ReinforcementLearningEnvironments.RewardTransformedEnv
— Type
RewardTransformedEnv(env, f)
Apply f
on reward(env)
.
#
ReinforcementLearningEnvironments.RockPaperScissorsEnv
— Type
RockPaperScissorsEnv()
Rock Paper Scissors is a simultaneous, zero sum game.
#
ReinforcementLearningEnvironments.StateCachedEnv
— Type
Cache the state so that state(env)
will always return the same result before the next interaction with env
. This function is useful because some environments are stateful during each state(env)
. For example: StateTransformedEnv(StackFrames(...))
.
#
ReinforcementLearningEnvironments.StateTransformedEnv
— Method
StateTransformedEnv(env; state_mapping=identity, state_space_mapping=identity)
state_mapping
will be applied on the original state when calling state(env)
, and similarly state_space_mapping
will be applied when calling state_space(env)
.
#
ReinforcementLearningEnvironments.StockTradingEnv
— Method
StockTradingEnv(;kw...)
This environment is originally provided in Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
Keyword Arguments
-
initial_account_balance=1_000_000
.
#
ReinforcementLearningEnvironments.TicTacToeEnv
— Method
TicTacToeEnv()
Create a new instance of the TicTacToe environment.
#
ReinforcementLearningEnvironments.TigerProblemEnv
— Type
TigerProblemEnv(;rng=Random>GLOBAL_RNG)
Here we use the The Tiger Proglem to demonstrate how to write a POMDP problem.
#
ReinforcementLearningEnvironments.TinyHanabiEnv
— Method
TinyHanabiEnv()
#
Random.seed!
— Method
The multi-arm bandits environment is a stochastic environment. The resulted reward may be different even after taking the same actions each time. So for this kind of environments, the Random.seed!(env)
must be implemented to help increase reproducibility without creating a new instance of the same rng
.
#
ReinforcementLearningBase.act!
— Method
In our design, the return of taking an action in env
is undefined. This is the main difference compared to those interfaces defined in OpenAI/Gym. We find that the async manner is more suitable to describe many complicated environments. However, one of the inconveniences is that we have to cache some intermediate data for future queries. Here we have to store reward
and is_terminated
in the instance of env
for future queries.
#
ReinforcementLearningBase.action_space
— Method
First we need to define the action space. In the MultiArmBanditsEnv
environment, the possible actions are 1
to k
(which equals to length(env.true_values)
).
Although we decide to return an action space of |
#
ReinforcementLearningBase.current_player
— Method
Note that although this is a two player game, the current player is always a dummy simultaneous player.
#
ReinforcementLearningBase.legal_action_space
— Method
In the first round, the guest has 3 options, in the second round only two options are valid, those different then the host’s action.
#
ReinforcementLearningBase.legal_action_space_mask
— Method
For environments of [FULL_ACTION_SET
], this function must be implemented.
#
ReinforcementLearningBase.reward
— Method
!!! warn
If the env
is not started yet, the returned value is meaningless. The reason why we don’t throw an exception here is to simplify the code logic to keep type consistency when storing the value in buffers.
#
ReinforcementLearningBase.state
— Method
Since MultiArmBanditsEnv
is just a one-shot game, it doesn’t matter what the state is after each action. So here we can simply set it to a constant 1
.
#
ReinforcementLearningBase.state
— Method
For multi-agent environments, we usually implement the most detailed one.
#
ReinforcementLearningBase.state
— Method
The main difference compared to other environments is that, now we have two kinds of states. The observation and the internal state. By default we return the observation.
#
ReinforcementLearningBase.state_space
— Method
Since it’s a one-shot game, the state space doesn’t have much meaning.
#
ReinforcementLearningEnvironments.discrete2standard_discrete
— Method
discrete2standard_discrete(env)
Convert an env
with a discrete action space to a standard form:
-
The action space is of type
Base.OneTo
-
If the
env
is ofFULL_ACTION_SET
, then each action in thelegal_action_space(env)
is also anInt
in the action space.
The standard form is useful for some algorithms (like Q-learning).