ReinforcementLearningEnvironments.jl

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.

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.

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`

DefaultStateStyleEnv{S}(env::E)

Reset the result of DefaultStateStyle without changing the original behavior.

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.

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

MaxTimeoutEnv(env::E, max_t::Int; current_t::Int = 1)

Force is_terminated(env) return true after max_t interactions.

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.

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

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.

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()

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.

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()

PigEnv(;n_players=2)

See wiki for explanation of this game.

Here we use it to demonstrate how to write a game with more than 2 players.

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:

1. The state space is more complicated (well, not that complicated though).
2. It's a sequential game of multiple action steps.
3. It's a deterministic game instead of stochastic game.

RewardOverriddenEnv(env, f)

Apply f on env to generate a custom reward.

RewardTransformedEnv(env, f)

Apply f on reward(env).

RockPaperScissorsEnv()

Rock Paper Scissors is a simultaneous, zero sum game.

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(...)).

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).

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.

TicTacToeEnv()

Create a new instance of the TicTacToe environment.

TigerProblemEnv(;rng=Random>GLOBAL_RNG)

Here we use the The Tiger Proglem to demonstrate how to write a POMDP problem.

TinyHanabiEnv()

See https://arxiv.org/abs/1902.00506.

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.

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.

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)).

Note that although this is a two player game, the current player is always a dummy simultaneous player.

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.

For environments of [FULL_ACTION_SET], this function must be implemented.

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.

For multi-agent environments, we usually implement the most detailed one.

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.

Since it's a one-shot game, the state space doesn't have much meaning.

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 of FULL_ACTION_SET, then each action in the legal_action_space(env) is also an Int in the action space.

The standard form is useful for some algorithms (like Q-learning).

install_gym(; packages = ["gym", "pybullet"])